Overview

Dataset statistics

Number of variables8
Number of observations12165
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory760.4 KiB
Average record size in memory64.0 B

Variable types

Numeric8

Alerts

per_area_buildings is highly overall correlated with per_area_greenery and 2 other fieldsHigh correlation
per_area_greenery is highly overall correlated with per_area_buildings and 2 other fieldsHigh correlation
per_residential_road is highly overall correlated with per_area_buildings and 2 other fieldsHigh correlation
per_rural_road is highly overall correlated with per_area_buildings and 2 other fieldsHigh correlation
publictransport_frequency has 3281 (27.0%) zerosZeros
per_area_greenery has 128 (1.1%) zerosZeros
per_area_water has 1319 (10.8%) zerosZeros
per_residential_road has 339 (2.8%) zerosZeros
per_rural_road has 4209 (34.6%) zerosZeros
per_highway has 10293 (84.6%) zerosZeros
per_active has 5444 (44.8%) zerosZeros

Reproduction

Analysis started2024-07-05 13:20:28.010271
Analysis finished2024-07-05 13:20:38.237464
Duration10.23 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

publictransport_frequency
Real number (ℝ)

ZEROS 

Distinct3894
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1581.8982
Minimum0
Maximum64805
Zeros3281
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size95.2 KiB
2024-07-05T15:20:38.453705image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median569
Q31811
95-th percentile6433.8
Maximum64805
Range64805
Interquartile range (IQR)1811

Descriptive statistics

Standard deviation3111.4158
Coefficient of variation (CV)1.9668875
Kurtosis68.587974
Mean1581.8982
Median Absolute Deviation (MAD)569
Skewness6.1786382
Sum19243792
Variance9680908.6
MonotonicityNot monotonic
2024-07-05T15:20:38.829437image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3281
 
27.0%
154 37
 
0.3%
40 27
 
0.2%
120 25
 
0.2%
156 21
 
0.2%
20 20
 
0.2%
80 19
 
0.2%
144 19
 
0.2%
112 17
 
0.1%
160 17
 
0.1%
Other values (3884) 8682
71.4%
ValueCountFrequency (%)
0 3281
27.0%
1 1
 
< 0.1%
2 7
 
0.1%
4 4
 
< 0.1%
5 6
 
< 0.1%
6 6
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
10 13
 
0.1%
11 4
 
< 0.1%
ValueCountFrequency (%)
64805 1
< 0.1%
63565 1
< 0.1%
53235 1
< 0.1%
51538 1
< 0.1%
50481 1
< 0.1%
40026 1
< 0.1%
40025 1
< 0.1%
39814 1
< 0.1%
36527 1
< 0.1%
35743 1
< 0.1%

per_area_greenery
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12038
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.490603
Minimum0
Maximum38.234096
Zeros128
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size95.2 KiB
2024-07-05T15:20:39.034313image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.0420724
Q14.8169636
median9.8612965
Q322.338196
95-th percentile32.327099
Maximum38.234096
Range38.234096
Interquartile range (IQR)17.521233

Descriptive statistics

Standard deviation10.455145
Coefficient of variation (CV)0.77499459
Kurtosis-1.0035911
Mean13.490603
Median Absolute Deviation (MAD)6.6561566
Skewness0.60359166
Sum164113.19
Variance109.31005
MonotonicityNot monotonic
2024-07-05T15:20:39.235924image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 128
 
1.1%
5.023506705 1
 
< 0.1%
25.31759689 1
 
< 0.1%
5.338038281 1
 
< 0.1%
25.62188288 1
 
< 0.1%
9.872763433 1
 
< 0.1%
3.76299303 1
 
< 0.1%
4.320276347 1
 
< 0.1%
6.506023141 1
 
< 0.1%
19.84677292 1
 
< 0.1%
Other values (12028) 12028
98.9%
ValueCountFrequency (%)
0 128
1.1%
3.311726637 × 10-51
 
< 0.1%
0.0002723611384 1
 
< 0.1%
0.000486538144 1
 
< 0.1%
0.001162506364 1
 
< 0.1%
0.001353928494 1
 
< 0.1%
0.001474690548 1
 
< 0.1%
0.001575551506 1
 
< 0.1%
0.002747688867 1
 
< 0.1%
0.004953091281 1
 
< 0.1%
ValueCountFrequency (%)
38.23409643 1
< 0.1%
37.04451075 1
< 0.1%
36.45912947 1
< 0.1%
36.38465915 1
< 0.1%
36.3407524 1
< 0.1%
36.3092597 1
< 0.1%
36.2772134 1
< 0.1%
36.26314952 1
< 0.1%
36.20298637 1
< 0.1%
36.12396057 1
< 0.1%

per_area_water
Real number (ℝ)

ZEROS 

Distinct10847
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2823684
Minimum0
Maximum27.855333
Zeros1319
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size95.2 KiB
2024-07-05T15:20:39.436821image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.13732814
median0.68954102
Q31.7885376
95-th percentile4.325278
Maximum27.855333
Range27.855333
Interquartile range (IQR)1.6512095

Descriptive statistics

Standard deviation1.7931206
Coefficient of variation (CV)1.3982882
Kurtosis23.393333
Mean1.2823684
Median Absolute Deviation (MAD)0.65188578
Skewness3.6735367
Sum15600.011
Variance3.2152814
MonotonicityNot monotonic
2024-07-05T15:20:39.621881image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1319
 
10.8%
1.798143226 1
 
< 0.1%
0.689403992 1
 
< 0.1%
0.004875666929 1
 
< 0.1%
0.06273675122 1
 
< 0.1%
1.596648584 1
 
< 0.1%
0.3092050764 1
 
< 0.1%
3.070741944 1
 
< 0.1%
0.9104452241 1
 
< 0.1%
0.1029947057 1
 
< 0.1%
Other values (10837) 10837
89.1%
ValueCountFrequency (%)
0 1319
10.8%
2.632668805 × 10-91
 
< 0.1%
4.321415953 × 10-61
 
< 0.1%
3.327274725 × 10-51
 
< 0.1%
4.429612922 × 10-51
 
< 0.1%
0.0001076551269 1
 
< 0.1%
0.0001555083661 1
 
< 0.1%
0.0002347983729 1
 
< 0.1%
0.0003422481464 1
 
< 0.1%
0.0005162115259 1
 
< 0.1%
ValueCountFrequency (%)
27.85533324 1
< 0.1%
20.68321406 1
< 0.1%
20.18943585 1
< 0.1%
19.94603458 1
< 0.1%
19.89993472 1
< 0.1%
19.24829146 1
< 0.1%
19.19504426 1
< 0.1%
19.07084837 1
< 0.1%
18.80182511 1
< 0.1%
17.40978801 1
< 0.1%

per_area_buildings
Real number (ℝ)

HIGH CORRELATION 

Distinct12163
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6291144
Minimum0
Maximum23.944334
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size95.2 KiB
2024-07-05T15:20:39.803312image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.21135528
Q11.1367835
median4.5718661
Q36.9271935
95-th percentile11.027933
Maximum23.944334
Range23.944334
Interquartile range (IQR)5.79041

Descriptive statistics

Standard deviation3.6049904
Coefficient of variation (CV)0.7787646
Kurtosis0.50375894
Mean4.6291144
Median Absolute Deviation (MAD)2.8313096
Skewness0.73364767
Sum56313.177
Variance12.995956
MonotonicityNot monotonic
2024-07-05T15:20:40.025039image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
< 0.1%
5.869768029 1
 
< 0.1%
0.6701708303 1
 
< 0.1%
0.138974796 1
 
< 0.1%
4.24016053 1
 
< 0.1%
6.255588561 1
 
< 0.1%
7.981797138 1
 
< 0.1%
9.182513199 1
 
< 0.1%
2.860787783 1
 
< 0.1%
1.455405271 1
 
< 0.1%
Other values (12153) 12153
99.9%
ValueCountFrequency (%)
0 3
< 0.1%
0.001292989649 1
 
< 0.1%
0.003463808939 1
 
< 0.1%
0.004593450547 1
 
< 0.1%
0.005332112859 1
 
< 0.1%
0.006402565116 1
 
< 0.1%
0.006660210548 1
 
< 0.1%
0.007195263077 1
 
< 0.1%
0.009291787888 1
 
< 0.1%
0.009328015381 1
 
< 0.1%
ValueCountFrequency (%)
23.94433391 1
< 0.1%
20.90929894 1
< 0.1%
20.73901565 1
< 0.1%
20.71522834 1
< 0.1%
20.52081539 1
< 0.1%
20.44454525 1
< 0.1%
20.27873077 1
< 0.1%
20.17997013 1
< 0.1%
19.900095 1
< 0.1%
19.8617785 1
< 0.1%

per_residential_road
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10193
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.214681
Minimum0
Maximum100
Zeros339
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size95.2 KiB
2024-07-05T15:20:40.236333image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.5609396
Q140.325524
median84.486774
Q397.049504
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)56.72398

Descriptive statistics

Standard deviation34.757084
Coefficient of variation (CV)0.50952497
Kurtosis-0.82117927
Mean68.214681
Median Absolute Deviation (MAD)15.434025
Skewness-0.85914991
Sum829831.6
Variance1208.0549
MonotonicityNot monotonic
2024-07-05T15:20:40.453061image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1635
 
13.4%
0 339
 
2.8%
94.11333111 1
 
< 0.1%
92.05567629 1
 
< 0.1%
88.23098141 1
 
< 0.1%
80.62746788 1
 
< 0.1%
27.84429706 1
 
< 0.1%
96.07284246 1
 
< 0.1%
99.26277723 1
 
< 0.1%
98.25999102 1
 
< 0.1%
Other values (10183) 10183
83.7%
ValueCountFrequency (%)
0 339
2.8%
3.314176031 × 10-51
 
< 0.1%
4.343944576 × 10-51
 
< 0.1%
4.579345788 × 10-51
 
< 0.1%
0.0006497087562 1
 
< 0.1%
0.001110703711 1
 
< 0.1%
0.002568085952 1
 
< 0.1%
0.00276358131 1
 
< 0.1%
0.003130729978 1
 
< 0.1%
0.004029930874 1
 
< 0.1%
ValueCountFrequency (%)
100 1635
13.4%
99.99999967 1
 
< 0.1%
99.99999787 1
 
< 0.1%
99.99999783 1
 
< 0.1%
99.99999594 1
 
< 0.1%
99.99998355 1
 
< 0.1%
99.99996971 1
 
< 0.1%
99.99995187 1
 
< 0.1%
99.99988571 1
 
< 0.1%
99.99972194 1
 
< 0.1%

per_rural_road
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7748
Distinct (%)63.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.177833
Minimum0
Maximum100
Zeros4209
Zeros (%)34.6%
Negative0
Negative (%)0.0%
Memory size95.2 KiB
2024-07-05T15:20:40.669608image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6.6565019
Q346.498914
95-th percentile94.873089
Maximum100
Range100
Interquartile range (IQR)46.498914

Descriptive statistics

Standard deviation33.199588
Coefficient of variation (CV)1.3186039
Kurtosis-0.34811395
Mean25.177833
Median Absolute Deviation (MAD)6.6565019
Skewness1.0852088
Sum306288.34
Variance1102.2127
MonotonicityNot monotonic
2024-07-05T15:20:40.852881image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4209
34.6%
100 210
 
1.7%
5.030005612 1
 
< 0.1%
6.297680703 1
 
< 0.1%
64.5721348 1
 
< 0.1%
15.6981469 1
 
< 0.1%
75.19335128 1
 
< 0.1%
54.23877035 1
 
< 0.1%
45.4432636 1
 
< 0.1%
2.051360471 1
 
< 0.1%
Other values (7738) 7738
63.6%
ValueCountFrequency (%)
0 4209
34.6%
3.332355882 × 10-71
 
< 0.1%
1.404330312 × 10-61
 
< 0.1%
2.12537106 × 10-61
 
< 0.1%
2.166527999 × 10-61
 
< 0.1%
3.462658065 × 10-61
 
< 0.1%
1.645306212 × 10-51
 
< 0.1%
3.029317791 × 10-51
 
< 0.1%
3.739411126 × 10-51
 
< 0.1%
0.0001902718774 1
 
< 0.1%
ValueCountFrequency (%)
100 210
1.7%
99.99996686 1
 
< 0.1%
99.99996442 1
 
< 0.1%
99.9988893 1
 
< 0.1%
99.99743191 1
 
< 0.1%
99.99723642 1
 
< 0.1%
99.99686927 1
 
< 0.1%
99.99597007 1
 
< 0.1%
99.99534537 1
 
< 0.1%
99.99440581 1
 
< 0.1%

per_highway
Real number (ℝ)

ZEROS 

Distinct1873
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5451418
Minimum0
Maximum82.256903
Zeros10293
Zeros (%)84.6%
Negative0
Negative (%)0.0%
Memory size95.2 KiB
2024-07-05T15:20:41.052807image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile20.122129
Maximum82.256903
Range82.256903
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.9015424
Coefficient of variation (CV)3.1045588
Kurtosis17.032417
Mean2.5451418
Median Absolute Deviation (MAD)0
Skewness3.8781027
Sum30961.65
Variance62.434372
MonotonicityNot monotonic
2024-07-05T15:20:41.235320image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10293
84.6%
8.479013226 1
 
< 0.1%
15.7300907 1
 
< 0.1%
6.98591715 1
 
< 0.1%
10.15742119 1
 
< 0.1%
6.109168997 1
 
< 0.1%
29.52474067 1
 
< 0.1%
41.29394978 1
 
< 0.1%
3.162760024 1
 
< 0.1%
25.00084838 1
 
< 0.1%
Other values (1863) 1863
 
15.3%
ValueCountFrequency (%)
0 10293
84.6%
3.557689311 × 10-51
 
< 0.1%
0.0009079742318 1
 
< 0.1%
0.001209058653 1
 
< 0.1%
0.001666389235 1
 
< 0.1%
0.003143286982 1
 
< 0.1%
0.0103021768 1
 
< 0.1%
0.0142213399 1
 
< 0.1%
0.01463804807 1
 
< 0.1%
0.01529223286 1
 
< 0.1%
ValueCountFrequency (%)
82.25690269 1
< 0.1%
73.20491197 1
< 0.1%
71.52998418 1
< 0.1%
70.16660165 1
< 0.1%
69.3804528 1
< 0.1%
66.9891535 1
< 0.1%
65.40662239 1
< 0.1%
64.55761819 1
< 0.1%
63.72402008 1
< 0.1%
63.62412044 1
< 0.1%

per_active
Real number (ℝ)

ZEROS 

Distinct6722
Distinct (%)55.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0212424
Minimum0
Maximum95.804116
Zeros5444
Zeros (%)44.8%
Negative0
Negative (%)0.0%
Memory size95.2 KiB
2024-07-05T15:20:41.418467image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.36389957
Q34.8246432
95-th percentile19.44305
Maximum95.804116
Range95.804116
Interquartile range (IQR)4.8246432

Descriptive statistics

Standard deviation7.6711414
Coefficient of variation (CV)1.9076545
Kurtosis17.984871
Mean4.0212424
Median Absolute Deviation (MAD)0.36389957
Skewness3.4924557
Sum48918.414
Variance58.84641
MonotonicityNot monotonic
2024-07-05T15:20:41.632377image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5444
44.8%
16.21401408 1
 
< 0.1%
3.648124337 1
 
< 0.1%
0.7063809931 1
 
< 0.1%
0.03019650263 1
 
< 0.1%
5.540871085 1
 
< 0.1%
8.231870725 1
 
< 0.1%
0.4305756285 1
 
< 0.1%
0.7061477781 1
 
< 0.1%
2.597070003 1
 
< 0.1%
Other values (6712) 6712
55.2%
ValueCountFrequency (%)
0 5444
44.8%
3.155973939 × 10-61
 
< 0.1%
4.059416741 × 10-61
 
< 0.1%
1.073265843 × 10-51
 
< 0.1%
1.078748776 × 10-51
 
< 0.1%
1.276030494 × 10-51
 
< 0.1%
0.0001142894781 1
 
< 0.1%
0.0001179909071 1
 
< 0.1%
0.0002780570312 1
 
< 0.1%
0.0002949521509 1
 
< 0.1%
ValueCountFrequency (%)
95.80411608 1
< 0.1%
85.22963503 1
< 0.1%
83.82159525 1
< 0.1%
78.91292917 1
< 0.1%
75.15056302 1
< 0.1%
74.72876896 1
< 0.1%
74.04506531 1
< 0.1%
73.42887719 1
< 0.1%
72.77528583 1
< 0.1%
72.43716033 1
< 0.1%

Interactions

2024-07-05T15:20:36.787704image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:28.425886image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:29.773904image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:31.126916image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:32.372631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:33.472409image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:34.587505image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:35.737960image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:36.933481image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:28.626857image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:29.973956image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:31.406532image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:32.506130image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:33.622346image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:34.721777image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:35.871689image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:37.088698image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:28.807272image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:30.206652image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:31.556423image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:32.656023image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:33.772429image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:34.872094image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:36.004558image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:37.221268image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:28.957164image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:30.428812image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:31.706594image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:32.772831image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:33.905713image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:35.022005image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:36.137979image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:37.354216image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:29.124095image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:30.573417image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:31.839440image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:32.905692image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:34.038664image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:35.154839image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:36.254818image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:37.487644image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:29.256852image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:30.706739image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:31.989703image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:33.056052image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:34.171791image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:35.338142image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:36.387890image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:37.620951image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:29.426711image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:30.856670image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:32.123624image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:33.206152image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:34.289016image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:35.471864image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:36.504859image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:37.737952image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:29.607213image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:30.989597image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:32.239530image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:33.338998image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:34.455851image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:35.603784image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T15:20:36.636796image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-07-05T15:20:41.769673image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
per_activeper_area_buildingsper_area_greeneryper_area_waterper_highwayper_residential_roadper_rural_roadpublictransport_frequency
per_active1.0000.051-0.0780.1300.030-0.154-0.1520.140
per_area_buildings0.0511.000-0.886-0.054-0.2590.756-0.7760.197
per_area_greenery-0.078-0.8861.000-0.0510.244-0.7390.772-0.176
per_area_water0.130-0.054-0.0511.0000.057-0.010-0.0560.062
per_highway0.030-0.2590.2440.0571.000-0.3240.1840.048
per_residential_road-0.1540.756-0.739-0.010-0.3241.000-0.8790.158
per_rural_road-0.152-0.7760.772-0.0560.184-0.8791.000-0.179
publictransport_frequency0.1400.197-0.1760.0620.0480.158-0.1791.000

Missing values

2024-07-05T15:20:37.903975image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-05T15:20:38.121537image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

publictransport_frequencyper_area_greeneryper_area_waterper_area_buildingsper_residential_roadper_rural_roadper_highwayper_active
04134.08.1243621.7981435.86976878.7559805.0300060.00000016.214014
13890.015.0306460.9411003.25491481.03892613.7068570.0000005.254217
24291.012.0828901.2138493.42707679.0667776.9538919.4818024.497531
30.030.2894920.7365280.62235826.22771745.6094050.00000028.162878
4700.027.4975930.6166170.3527974.49558088.1346555.2928622.076903
50.032.7456950.5833320.24759111.47844068.5081988.92679711.086565
62440.02.8936591.7376579.02460295.9507190.0000000.0000004.049281
70.02.5022592.7274488.016825100.0000000.0000000.0000000.000000
81664.010.3129350.2680953.88842797.7678831.2668050.0000000.965312
9206.07.7116971.0975304.19193891.6434978.3565030.0000000.000000
publictransport_frequencyper_area_greeneryper_area_waterper_area_buildingsper_residential_roadper_rural_roadper_highwayper_active
121550.034.8141691.4214200.1426430.12055874.7259340.025.153508
121560.07.5570121.5582905.63809389.52630510.4736950.00.000000
121570.032.8575241.0695120.37106240.17684759.8231530.00.000000
121586115.05.9923180.9165960.8836311.98887398.0111270.00.000000
121590.034.7465101.9982550.2406790.00458844.5090400.055.486372
121601825.08.4749281.4319344.84154993.4604720.9574140.05.582114
121610.08.3527120.4336077.11402453.2565260.0000000.046.743474
12162414.02.3726570.8723487.43503787.8045349.4414290.02.754037
12163297.016.3956742.2621313.8190060.26240399.7375970.00.000000
121640.017.3580853.8871013.68899199.8585330.1414670.00.000000